PolarProtPred: Predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning

Optimized Biotinylated Peptide Detection Method for Characterizing the Cell Surface Proteome

Cell surface proteins (CSPs) play a pivotal role in cellular processes and are crucial for differentiating various cell types. Despite the significance of the cell surface-localized proteome, it is often characterized using low-throughput techniques that provide limited information from the surface-exposed protein segments of individual cells. If such information is available, it is often scattered across numerous publications, making it time-consuming to gather. To address these challenges, we present a high-throughput method that utilizes biotinylation of accessible primary amino groups within the extracellular regions of CSPs to simultaneously generate several hundred CSP-specific peptides in a single experiment. The steps of the method have been meticulously refined to capture an increasing number of cell type-specific biotinylated peptides localized in cell surface accessible protein regions.The protocol developed is readily applicable to the analysis of both soluble and adherent cell surface proteins as described in detailed in this paper. Additionally, the method can be used for side-selective protein biotinylation on biological barrier-forming epithelial cells, allowing the qualitative and quantitative characterization of proteins in both their apical and basolateral membranes. The identified amino-modified surface peptides derived from CSPs have potential as targets for various molecular biology research applications, including antibody staining, inhibitor development and monitoring of host-pathogen interactions. Furthermore, label-free quantification of CSPs via mass spectrometry (based on identified peptide list) provides valuable insights into their relative abundance in different cell surface or plasma membrane regions.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.